Combinatorial Auctions with Conflict-Based Externalities

  • Yun Kuen Cheung
  • Monika Henzinger
  • Martin Hoefer
  • Martin Starnberger
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9470)

Abstract

Combinatorial auctions (CA) are a well-studied area in algorithmic mechanism design. However, contrary to the standard model, empirical studies suggest that a bidder’s valuation often does not depend solely on the goods assigned to him. For instance, in adwords auctions an advertiser might not want his ads to be displayed next to his competitors’ ads. In this paper, we propose and analyze several natural graph-theoretic models that incorporate such negative externalities, in which bidders form a directed conflict graph with maximum out-degree \(\varDelta \). We design algorithms and truthful mechanisms for social welfare maximization that attain approximation ratios depending on \(\varDelta \).

For CA, our results are twofold: (1) A lottery that eliminates conflicts by discarding bidders/items independent of the bids. It allows to apply any truthful \(\alpha \)-approximation mechanism for conflict-free valuations and yields an \({\mathcal O}(\alpha \varDelta )\)-approximation mechanism. (2) For fractionally sub-additive valuations, we design a rounding algorithm via a novel combination of a semi-definite program and a linear program, resulting in a cone program; the approximation ratio is \({\mathcal O}((\varDelta \log \log \varDelta )/\log \varDelta )\). The ratios are almost optimal given existing hardness results.

For adwords auctions, we present several algorithms for the most relevant scenario when the number of items is small. In particular, we design a truthful mechanism with approximation ratio \(o(\varDelta )\) when the number of items is only logarithmic in the number of bidders.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Open Access This chapter is distributed under the terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Authors and Affiliations

  • Yun Kuen Cheung
    • 1
  • Monika Henzinger
    • 1
  • Martin Hoefer
    • 2
  • Martin Starnberger
    • 1
  1. 1.Faculty of Computer ScienceUniversity of ViennaViennaAustria
  2. 2.Max-Planck-Institut für InformatikSaarland UniversitySaarbrückenGermany

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